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Resource Library
Seasonal Adjustment Working Papers
Seasonal Adjustment Working Papers
This page provides papers of interest to researchers and seasonal adjustment experts are available for downloading.
Working Paper
Forecasting Continuous-Time Processes to Signal Extraction
The paper derives forecasting and signal extraction estimates for continuous time processes.
Working Paper
The Perils of Inferring Serial Dependence from Sample Autocorrelations
We demonstrate that oscillatory patterns in the higher lags of sample autocorrelations can arise whenever the true process is a finite order MA.
Working Paper
A Conversation with David Findley
David Findley has published more than 40 journal articles and book chapters, as well as dozens of technical reports and conference proceedings.
Working Paper
An Alternative Model-Based Seasonal Adjustment
This paper provides empirical documentation of the occurrence of negative seasonal autocorrelation in seasonally adjusted data.
Working Paper
Bayesian Seasonal Adjustment of Long Memory Time Series
Existing approaches to the seasonal adjustment of economic time series are typically either nonparametric or model-based.
Working Paper
Comparing X-12-ARIMA and Model-Based Seasonal Adjustment Filters
We suggest a different approach to comparing filters by computing the MSE when using an X-12-ARIMA filter for estimating the underlying seasonal component.
Working Paper
Computation of Autocovariances for Generalized Gegenbauer Processes
Gegenbauer processes and their generalizations represent a general way of modeling long memory and seasonal long memory.
Working Paper
Relationships Between Month-to-Month and Year-to-Year Changes
When a monthly indicator contracts sharply for a few months and then starts to recover, the annual and monthly growth rates can give conflicting signals.
Working Paper
Stock Series Holiday Regressors from Flow Series Holiday Regressors
In this article, we derive holiday regressors for stock series from cumulative sums of flow-series holiday regressors.
Page Last Revised - October 28, 2021
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